Search result: Catalogue data in Spring Semester 2021
|Robotics, Systems and Control Master|
|151-0116-10L||High Performance Computing for Science and Engineering (HPCSE) for Engineers II||W||4 credits||4G||P. Koumoutsakos, S. M. Martin|
|Abstract||This course focuses on programming methods and tools for parallel computing on multi and many-core architectures. Emphasis will be placed on practical and computational aspects of Uncertainty Quantification and Propagation including the implementation of relevant algorithms on HPC architectures.|
|Objective||The course will teach |
- programming models and tools for multi and many-core architectures
- fundamental concepts of Uncertainty Quantification and Propagation (UQ+P) for computational models of systems in Engineering and Life Sciences
|Content||High Performance Computing:|
- Advanced topics in shared-memory programming
- Advanced topics in MPI
- GPU architectures and CUDA programming
- Uncertainty quantification under parametric and non-parametric modeling uncertainty
- Bayesian inference with model class assessment
- Markov Chain Monte Carlo simulation
Class notes, handouts
|Literature||- Class notes|
- Introduction to High Performance Computing for Scientists and Engineers, G. Hager and G. Wellein
- CUDA by example, J. Sanders and E. Kandrot
- Data Analysis: A Bayesian Tutorial, D. Sivia and J. Skilling
- An introduction to Bayesian Analysis - Theory and Methods, J. Gosh, N. Delampady and S. Tapas
- Bayesian Data Analysis, A. Gelman, J. Carlin, H. Stern, D. Dunson, A. Vehtari and D. Rubin
- Machine Learning: A Bayesian and Optimization Perspective, S. Theodorides
|Prerequisites / Notice||Students must be familiar with the content of High Performance Computing for Science and Engineering I (151-0107-20L)|
|151-0306-00L||Visualization, Simulation and Interaction - Virtual Reality I||W||4 credits||4G||A. Kunz|
|Abstract||Technology of Virtual Reality. Human factors, Creation of virtual worlds, Lighting models, Display- and acoustic- systems, Tracking, Haptic/tactile interaction, Motion platforms, Virtual prototypes, Data exchange, VR Complete systems, Augmented reality, Collaboration systems; VR and Design; Implementation of the VR in the industry; Human Computer Interfaces (HCI).|
|Objective||The product development process in the future will be characterized by the Digital Product which is the center point for concurrent engineering with teams spreas worldwide. Visualization and simulation of complex products including their physical behaviour at an early stage of development will be relevant in future. The lecture will give an overview to techniques for virtual reality, to their ability to visualize and to simulate objects. It will be shown how virtual reality is already used in the product development process.|
• Students are able to evaluate and select the most appropriate VR technology for a given task regarding:
o Visualization technologies displays/projection systems/head-mounted displays
o Tracking systems (inertia/optical/electromagnetic)
o Interaction technologies (sensing gloves/real walking/eye tracking/touch/etc.)
• Students are able to develop a VR application
• Students are able to apply VR to industrial needs
• Students will be able to apply the gained knowledge to a practical realization
• Students will be able to compare different operation principles (VR/AR/MR/XR)
|Content||Introduction to the world of virtual reality; development of new VR-techniques; introduction to 3D-computergraphics; modelling; physical based simulation; human factors; human interaction; equipment for virtual reality; display technologies; tracking systems; data gloves; interaction in virtual environment; navigation; collision detection; haptic and tactile interaction; rendering; VR-systems; VR-applications in industry, virtual mockup; data exchange, augmented reality.|
|Lecture notes||A complete version of the handout is also available in English.|
|Prerequisites / Notice||Voraussetzungen:|
Vorlesung geeignet für D-MAVT, D-ITET, D-MTEC und D-INF
Testat/ Kredit-Bedingungen/ Prüfung:
– Teilnahme an Vorlesung und Kolloquien
– Erfolgreiche Durchführung von Übungen in Teams
– Mündliche Einzelprüfung 30 Minuten
|151-0534-00L||Advanced Dynamics||W||4 credits||3V + 1U||P. Tiso|
|Abstract||Lagrangian dynamics - Principle of virtual work and virtual power - holonomic and non holonomic contraints - 3D rigid body dynamics - equilibrium - linearization - stability - vibrations - frequency response|
|Objective||This course provides the students of mechanical engineering with fundamental analytical mechanics for the study of complex mechanical systems .We introduce the powerful techniques of principle of virtual work and virtual power to systematically write the equation of motion of arbitrary systems subjected to holonomic and non-holonomic constraints. The linearisation around equilibrium states is then presented, together with the concept of linearised stability. Linearized models allow the study of small amplitude vibrations for unforced and forced systems. For this, we introduce the concept of vibration modes and frequencies, modal superposition and modal truncation. The case of the vibration of light damped systems is discussed. The kinematics and dynamics of 3D rigid bodies is also extensively treated.|
|Lecture notes||Lecture notes are produced in class and are downloadable right after each lecture.|
|Literature||The students will prepare their own notes. A copy of the lecture notes will be available.|
|Prerequisites / Notice||Mechanics III or equivalent; Analysis I-II, or equivalent; Linear Algebra I-II, or equivalent.|
|151-0566-00L||Recursive Estimation||W||4 credits||2V + 1U||R. D'Andrea|
|Abstract||Estimation of the state of a dynamic system based on a model and observations in a computationally efficient way.|
|Objective||Learn the basic recursive estimation methods and their underlying principles.|
|Content||Introduction to state estimation; probability review; Bayes' theorem; Bayesian tracking; extracting estimates from probability distributions; Kalman filter; extended Kalman filter; particle filter; observer-based control and the separation principle.|
|Lecture notes||Lecture notes available on course website: http://www.idsc.ethz.ch/education/lectures/recursive-estimation.html|
|Prerequisites / Notice||Requirements: Introductory probability theory and matrix-vector algebra.|
|151-0593-00L||Embedded Control Systems||W||4 credits||6G||J. S. Freudenberg, M. Schmid Daners|
|Abstract||This course provides a comprehensive overview of embedded control systems. The concepts introduced are implemented and verified on a microprocessor-controlled haptic device.|
|Objective||Familiarize students with main architectural principles and concepts of embedded control systems.|
|Content||An embedded system is a microprocessor used as a component in another piece of technology, such as cell phones or automobiles. In this intensive two-week block course the students are presented the principles of embedded digital control systems using a haptic device as an example for a mechatronic system. A haptic interface allows for a human to interact with a computer through the sense of touch.|
Subjects covered in lectures and practical lab exercises include:
- The application of C-programming on a microprocessor
- Digital I/O and serial communication
- Quadrature decoding for wheel position sensing
- Queued analog-to-digital conversion to interface with the analog world
- Pulse width modulation
- Timer interrupts to create sampling time intervals
- System dynamics and virtual worlds with haptic feedback
- Introduction to rapid prototyping
|Lecture notes||Lecture notes, lab instructions, supplemental material|
|Prerequisites / Notice||Prerequisite courses are Control Systems I and Informatics I.|
This course is restricted to 33 students due to limited lab infrastructure. Interested students please contact Marianne Schmid Daners (E-Mail: firstname.lastname@example.org)
After your reservation has been confirmed please register online at www.mystudies.ethz.ch.
Detailed information can be found on the course website
|151-0630-00L||Nanorobotics||W||4 credits||2V + 1U||S. Pané Vidal|
|Abstract||Nanorobotics is an interdisciplinary field that includes topics from nanotechnology and robotics. The aim of this course is to expose students to the fundamental and essential aspects of this emerging field.|
|Objective||The aim of this course is to expose students to the fundamental and essential aspects of this emerging field. These topics include basic principles of nanorobotics, building parts for nanorobotic systems, powering and locomotion of nanorobots, manipulation, assembly and sensing using nanorobots, molecular motors, and nanorobotics for nanomedicine.|
|151-0634-00L||Perception and Learning for Robotics |
Number of participants limited to: 30
To apply for the course please create a CV in pdf of max. 2 pages, including your machine learning and/or robotics experience. Please send the pdf to email@example.com for approval.
|W||4 credits||9A||C. D. Cadena Lerma, J. J. Chung|
|Abstract||This course covers tools from statistics and machine learning enabling the participants to deploy these algorithms as building blocks for perception pipelines on robotic tasks. All mathematical methods provided within the course will be discussed in context of and motivated by example applications mostly from robotics. The main focus of this course are student projects on robotics.|
|Objective||Applying Machine Learning methods for solving real-world robotics problems.|
|Content||Deep Learning for Perception; (Deep) Reinforcement Learning; Graph-Based Simultaneous Localization and Mapping|
|Lecture notes||Slides will be made available to the students.|
|Literature||Will be announced in the first lecture.|
|Prerequisites / Notice||The students are expected to be familiar with material of the "Recursive Estimation" and the "Introduction to Machine Learning" lectures. Particularly understanding of basic machine learning concepts, stochastic gradient descent for neural networks, reinforcement learning basics, and knowledge of Bayesian Filtering are required. Furtheremore, good knowledge of programming in C++ and Python is required.|
|151-0636-00L||Soft and Biohybrid Robotics||W||4 credits||3G||R. Katzschmann|
|Abstract||Soft robotics takes inspiration from nature and produces systems that are inherently safer to interact with. The class teaches processes involved in creating the structures, actuators, sensors, mechanical models, controllers, and machine learning models exploiting the deformable structure of soft robots in challenging tasks.|
(Class fully online via Zoom)
|Objective||Learn about the processes involved in creating soft and biohybrid robotic structures, actuator, sensors, mechanical models, closed-loop controllers, and machine learning approaches. Understand how to exploit the structural impedance and the dynamics of soft and biohybrid robots in locomotion and object manipulation tasks. Demonstrated learned capabilities in either a simulation or a physical prototype built at home.|
|Content||Students will gain experience on a range of soft technologies and a model-based approaches to design and simulation of soft continuum robots.|
0) Semester-long take-home project requiring students to implement the skills and knowledge learned during the class by building their own soft robotic prototype or simulation
1) Functional and intelligent materials for use in soft and biohybrid robotic applications
2) Design and design morphologies of soft robotic actuators and sensors
3) Fabrication techniques including 3D printing, casting, roll-to-roll, tissue engineering
4) Mechanical modeling including minimal parameter models, finite-element models and ML-based models
5) Closed-loop controllers of soft robots that exploit the robot's impedance and dynamics for locomotion and manipulation tasks
6) Deep Learning approaches to soft robotics, for design synthesis, modeling, and control
(Class offered only online via Zoom, see Moodle for details)
|Lecture notes||All class materials including slides, recordings, class challenges infos, pre-reads, and tutorial summaries can be found on Moodle: https://moodle-app2.let.ethz.ch/course/view.php?id=14501|
|Literature||1) Wang, Liyu, Surya G. Nurzaman, and Fumiya Iida. "Soft-material robotics." (2017).|
2) Polygerinos, Panagiotis, et al. "Soft robotics: Review of fluid‐driven intrinsically soft devices; manufacturing, sensing, control, and applications in human‐robot interaction." Advanced Engineering Materials 19.12 (2017): 1700016.
3) Verl, Alexander, et al. Soft Robotics. Berlin, Germany:: Springer, 2015.
4) Cianchetti, Matteo, et al. "Biomedical applications of soft robotics." Nature Reviews Materials 3.6 (2018): 143-153.
5) Ricotti, Leonardo, et al. "Biohybrid actuators for robotics: A review of devices actuated by living cells." Science Robotics 2.12 (2017).
6) Sun, Lingyu, et al. "Biohybrid robotics with living cell actuation." Chemical Society Reviews 49.12 (2020): 4043-4069.
|Prerequisites / Notice||(Robot) dynamics, control systems, introduction to robotics, materials for engineers. |
Only for students at master or PhD level.
Class size limitation is at 40 students.
|151-0641-00L||Introduction to Robotics and Mechatronics |
Number of participants limited to 45.
Enrollment is only valid through registration on the MSRL website (www.msrl.ethz.ch). Registrations per e-mail is no longer accepted!
|W||4 credits||2V + 2U||B. Nelson, N. Shamsudhin|
|Abstract||The aim of this lecture is to expose students to the fundamentals of mechatronic and robotic systems. Over the course of these lectures, topics will include how to interface a computer with the real world, different types of sensors and their use, different types of actuators and their use.|
|Objective||An ever-increasing number of mechatronic systems are finding their way into our daily lives. Mechatronic systems synergistically combine computer science, electrical engineering, and mechanical engineering. Robotics systems can be viewed as a subset of mechatronics that focuses on sophisticated control of moving devices. |
The aim of this course is to practically and theoretically expose students to the fundamentals of mechatronic and robotic systems. Over the course of the semester, the lecture topics will include an overview of robotics, an introduction to different types of sensors and their use, the programming of microcontrollers and interfacing these embedded computers with the real world, signal filtering and processing, an introduction to different types of actuators and their use, an overview of computer vision, and forward and inverse kinematics. Throughout the course, students will periodically attend laboratory sessions and implement lessons learned during lectures on real mechatronic systems. By the end of the course, you will be able to independently choose, design and integrate these different building blocks into a working mechatronic system.
|Content||The course consists of weekly lectures and lab sessions. The weekly topics are the following:|
0. Course Introduction
1. C Programming
3. Data Acquisition
4. Signal Processing
5. Digital Filtering
7. Computer Vision and Kinematics
8. Modeling and Control
9. Review and Outlook
The lecture schedule can be found on our course page on the MSRL website (www.msrl.ethz.ch)
|Prerequisites / Notice||The students are expected to be familiar with C programming.|
|151-0660-00L||Model Predictive Control||W||4 credits||2V + 1U||M. Zeilinger, A. Carron|
|Abstract||Model predictive control is a flexible paradigm that defines the control law as an optimization problem, enabling the specification of time-domain objectives, high performance control of complex multivariable systems and the ability to explicitly enforce constraints on system behavior. This course provides an introduction to the theory and practice of MPC and covers advanced topics.|
|Objective||Design and implement Model Predictive Controllers (MPC) for various system classes to provide high performance controllers with desired properties (stability, tracking, robustness,..) for constrained systems.|
|Content||- Review of required optimal control theory|
- Basics on optimization
- Receding-horizon control (MPC) for constrained linear systems
- Theoretical properties of MPC: Constraint satisfaction and stability
- Computation: Explicit and online MPC
- Practical issues: Tracking and offset-free control of constrained systems, soft constraints
- Robust MPC: Robust constraint satisfaction
- Nonlinear MPC: Theory and computation
- Hybrid MPC: Modeling hybrid systems and logic, mixed-integer optimization
- Simulation-based project providing practical experience with MPC
|Lecture notes||Script / lecture notes will be provided.|
|Prerequisites / Notice||One semester course on automatic control, Matlab, linear algebra.|
Courses on signals and systems and system modeling are recommended. Important concepts to start the course: State-space modeling, basic concepts of stability, linear quadratic regulation / unconstrained optimal control.
Expected student activities: Participation in lectures, exercises and course project; homework (~2hrs/week).
|151-0854-00L||Autonomous Mobile Robots||W||5 credits||4G||R. Siegwart, M. Chli, N. Lawrance|
|Abstract||The objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, environment perception, and probabilistic environment modeling, localizatoin, mapping and navigation. Theory will be deepened by exercises with small mobile robots and discussed accross application examples.|
|Objective||The objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, environment perception, and probabilistic environment modeling, localizatoin, mapping and navigation.|
|Lecture notes||This lecture is enhanced by around 30 small videos introducing the core topics, and multiple-choice questions for continuous self-evaluation. It is developed along the TORQUE (Tiny, Open-with-Restrictions courses focused on QUality and Effectiveness) concept, which is ETH's response to the popular MOOC (Massive Open Online Course) concept.|
|Literature||This lecture is based on the Textbook: |
Introduction to Autonomous Mobile Robots
Roland Siegwart, Illah Nourbakhsh, Davide Scaramuzza, The MIT Press, Second Edition 2011, ISBN: 978-0262015356
|151-9904-00L||Applied Compositional Thinking for Engineers||W||4 credits||3G||E. Frazzoli, A. Censi, J. Lorand|
|Abstract||This course is an introduction to applied category theory specifically targeted at persons with an engineering background. We focus on the benefits of applied category theory for thinking explicitly about abstraction and compositionality. The course will favor a computational/constructive approach, with concrete exercises in the Python.|
|Objective||In many domains of engineering it would be beneficial to think explicitly about abstraction and compositionality, to improve both the understanding of the problem and the design of the solution. However, the problem is that the type of math which could be useful to engineers is not traditionally taught.|
Applied category theory could help a lot, but it is quite unreachable by the average engineer. Recently many good options appeared for learning applied category theory; but none satisfy the two properties of 1) being approachable; and 2) highlighting how applied category theory can be used to formalize and solve concrete problems of interest to engineers.
This course will fill this gap. This course's goal is not to teach category theory for the sake of it. Rather, we want to teach the "compositionality way of thinking" to engineers; category theory will be just the means towards it. This implies that the presentation of materials sometimes diverges from the usual way to teach category theory; and some common concepts might be de-emphasized in favor of more obscure concepts that are more useful to an engineer.
The course will favor a computational/constructive approach: each concept is accompanied by concrete exercises in the programming language Python.
Throughout the course, we will discuss many examples related to autonomous robotics, because it is at the intersection of many branches of engineering: we can talk about hardware (sensing, actuation, communication) and software (perception, planning, learning, control) and their composition.
|Content||## Intended learning outcomes|
# Algebraic structures
The student is able to recognize algebraic structure for a familiar engineering domain. In particular we will recall
the following structures: monoid, groups, posets, monoidal posets, graphs.
The student is able to translate such algebraic structure in a concrete implementation using the Python language for the purpose of solving a computational problem.
# Categories and morphisms
The student is able to recognize categorical structure for a familiar engineering domain, understand the notion of object, morphism, homsets, and the properties of associativity and unitality.
The student is able to quickly spot non-categories (formalizations in which one of the axioms fails, possibly in a subtle way) and is informed that there exist possible generalizations (not studied in the course).
The student is able to translate a categorical structure into a concrete implementation using the Python language.
The student is able to recognize the categorical structure in the basic algebraic structures previously considered.
The student is able to use string diagrams to represent morphisms; and to write a Python program to draw such a representation.
# Products, coproducts, universality
# Recognizing and using additional structure
The student is able to spot the presence of the following structures: Monoidal structure, Feedback structure (Trace),
Locally posetal/lattice structure , Dagger/involutive structure.
# Functorial structure.
The student is able to recognize functorial structures in a familiar engineering domain.
The student can understand when there is a functorial structure between instances of a problem and solutions of the problem, and use such structure to write programs that use these compositionality structures to achieve either more elegance or efficiency (or both).
# The ladder of abstractions
The student is able to think about scenarios in which one can climb the ladder of abstractions. For example, the morphisms in a category can be considered objects in another category.
# Compact closed structure.
The student knows co-design theory (boolean profunctors + extensions) and how to use it to formalize design problems in their area of expertise.
The student knows how to use the basics of the MCPD language and use it to solve co-design problems.
# Rosetta stone
The student understands explicitly the connection between logic and category theory and can translate concepts back and forth.
The student understands explicitly the constructive nature of the presentation of category theory given so far.
The student is able to understand what is an "equational theory" and how to use it concretely.
The student understands the notion of substructural logics; the notion of polycategories; and linear logic. Mention of *-autonomous categories.
The student can translate the above in an implementation.
# Monadic structure
The student is able to recognize a monadic structure in the problem.
# Operads and operad-like structures.
|Lecture notes||Slides and notes will be provided.|
|Literature||B. Fong, D.I. Spivak, Seven Sketches in Compositionality: An Invitation to Applied Category Theory (https://arxiv.org/pdf/1803.05316)|
A. Censi, D. I. Spivak, J. Tan, G. Zardini, Mathematical Foundations of Engineering Co-Design (Own manuscript, to be published)
|Prerequisites / Notice||Algebra: at the level of a bachelor’s degree in engineering.|
Analysis: ODEs, dynamical systems.
Familiarity with basic physics, electrical engineering, mechanical engineering, mechatronics concepts (at the level of bachelor's degree in engineering).
Basics of Python programming.
|151-1115-00L||Aircraft Aerodynamics and Flight Mechanics||W||4 credits||3G||J. Wildi|
|Abstract||Equations of motion. Aircraft flight perfomance, flight envelope. Aircraft static stability and control, longituadinal and lateral stbility. Dynamic longitudinal and lateral stability.|
Flight test. Wind tunnel test.
|Objective||- Knowledge of methods to solve flight mechanic problems|
- To be able to apply basic methods for flight performence calculation and stability investigations
- Basic knowledge of flight and wind tunnel tests and test evaluation methods
|Content||Equations of motion. Aircraft flight perfomance, flight envelope. Aircraft static stability and control, longituadinal and lateral stbility. Dynamic longitudinal and lateral stability.|
Flight testing. Wind tunnel testing.
|Lecture notes||Ausgewählte Kapitel der Flugtechnik (J. Wildi)|
|Literature||Mc Cormick, B.W.: Aerodynamics, Aeronautics and Flight Mechanics (John Wiley and Sons), 1979 / 1995|
Anderson, J: Fundamentals of Aerodynamics (McGraw-Hill Comp Inc), 2010
|Prerequisites / Notice||Recommended: Lecture 'Basics of Aircraft und Vehicle Aerodynamics' (FS)|
|101-0521-10L||Machine Learning for Predictive Maintenance Applications |
The number of participants in the course is limited to 25 students.
Students interested in attending the lecture are requested to upload their transcript and a short motivation responding the following two questions (max. 200 words):
-How does this course fit to the other courses you have attended so far?
-How does the course support you in achieving your goal?
The following link can be used to upload the documents.
|W||8 credits||4G||O. Fink|
|Abstract||The course aims at developing machine learning algorithms that are able to use condition monitoring data efficiently and detect occurring faults in complex industrial assets, isolate their root cause and ultimately predict the remaining useful lifetime.|
|Objective||Students will |
- be able to understand the main challenges faced by predictive maintenance systems
- learn to extract relevant features from condition monitoring data
-learn to select appropriate machine learning algorithms for fault detection, diagnostics and prognostics
-learn to define the learning problem in way that allows its solution based on existing constrains such as lack of fault samples.
- learn to design end-to-end machine learning algorithms for fault detection and diagnostics
-be able to evaluate the performance of the applied algorithms.
At the end of the course, the students will be able to design data-driven predictive maintenance applications for complex engineered systems from raw condition monitoring data.
|Content||Early and reliable detection, isolation and prediction of faulty system conditions enables the operators to take recovery actions to prevent critical system failures and ensure a high level of availability and safety. This is particularly crucial for complex systems such as infrastructures, power plants and aircraft engines. Therefore, their system condition is increasingly tightly monitored by a large number of diverse condition monitoring sensors. With the increased availability of data on system condition on the one hand, and the increased complexity of explicit system physics-based models on the other hand, the application of data-driven approaches for predictive maintenance has been recently increasing. |
This course provides insights and hands-on experience in selecting, designing, optimizing and evaluating machine learning algorithms to tackle the challenges faced by maintenance systems of complex engineered systems.
Specific topics include:
-Introduction to condition monitoring and predictive maintenance systems
-Feature extraction and selection methodology
-Machine learning algorithms for fault detection and fault isolation
-End-to-end learning architectures (including feature learning) for fault detection and fault isolation
-Unsupervised and semi-supervised learning algorithms for predictive maintenance
-Machine learning algorithms for prediction of the remaining useful life
-Predictive maintenance systems at fleet level
-Domain adaptation for fault diagnostics
-Introduction to decision support systems for maintenance applications
|Lecture notes||Slides and other materials will be available online.|
|Literature||Relevant scientific papers will be discussed in the course.|
|Prerequisites / Notice||Strong analytical skills.|
Programming skills in python are strongly recommended.
|103-0848-00L||Industrial Metrology and Machine Vision |
Number of participants limited to 30.
|W||4 credits||3G||K. Schindler, D. Salido Monzú|
|Abstract||This course introduces contact and non-contact techniques for 3D coordinate, shape and motion determination as used for 3D inspection, dimensional control, reverse engineering, motion capture and similar industrial applications.|
|Objective||Understanding the physical basis of photographic sensors and imaging; familiarization with a broader view of image-based 3D geometry estimation beyond the classical photogrammetric approach; understanding the concepts of measurement traceability and uncertainty; acquiring an overview of general 3D image metrology including contact and non-contact techniques (coordinate measurement machines; optical tooling; laser-based high-precision instruments).|
|Content||CCD and CMOS technology; structured light and active stereo; shading models, shape from shading and photometric stereo; shape from focus; laser interferometry, laser tracker, laser radar; contact and non-contact coordinate measurement machines; optical tooling; measurement traceability, measurement uncertainty, calibration of measurement systems; 3d surface representations; case studies.|
|Lecture notes||Lecture slides and further literature will be made available on the course webpage.|
|227-0207-00L||Nonlinear Systems and Control |
Prerequisite: Control Systems (227-0103-00L)
|W||6 credits||4G||E. Gallestey Alvarez, P. F. Al Hokayem|
|Abstract||Introduction to the area of nonlinear systems and their control. Familiarization with tools for analysis of nonlinear systems. Discussion of the various nonlinear controller design methods and their applicability to real life problems.|
|Objective||On completion of the course, students understand the difference between linear and nonlinear systems, know the mathematical techniques for analysing these systems, and have learnt various methods for designing controllers accounting for their characteristics.|
Course puts the student in the position to deploy nonlinear control techniques in real applications. Theory and exercises are combined for better understanding of the virtues and drawbacks present in the different methods.
|Content||Virtually all practical control problems are of nonlinear nature. In some cases application of linear control methods leads to satisfactory controller performance. In many other cases however, only application of nonlinear analysis and control synthesis methods will guarantee achievement of the desired objectives. |
During the past decades mature nonlinear controller design methods have been developed and have proven themselves in applications. After an introduction of the basic methods for analysing nonlinear systems, these methods will be introduced together with a critical discussion of their pros and cons. Along the course the students will be familiarized with the basic concepts of nonlinear control theory.
This course is designed as an introduction to the nonlinear control field and thus no prior knowledge of this area is required. The course builds, however, on a good knowledge of the basic concepts of linear control and mathematical analysis.
|Lecture notes||An english manuscript will be made available on the course homepage during the course.|
|Literature||H.K. Khalil: Nonlinear Systems, Prentice Hall, 2001.|
|Prerequisites / Notice||Prerequisites: Linear Control Systems, or equivalent.|
|227-0216-00L||Control Systems II||W||6 credits||4G||R. Smith|
|Abstract||Introduction to basic and advanced concepts of modern feedback control.|
|Objective||Introduction to basic and advanced concepts of modern feedback control.|
|Content||This course is designed as a direct continuation of the course "Regelsysteme" (Control Systems). The primary goal is to further familiarize students with various dynamic phenomena and their implications for the analysis and design of feedback controllers. Simplifying assumptions on the underlying plant that were made in the course "Regelsysteme" are relaxed, and advanced concepts and techniques that allow the treatment of typical industrial control problems are presented. Topics include control of systems with multiple inputs and outputs, control of uncertain systems (robustness issues), limits of achievable performance, and controller implementation issues.|
|Lecture notes||The slides of the lecture are available to download.|
|Literature||Skogestad, Postlethwaite: Multivariable Feedback Control - Analysis and Design. Second Edition. John Wiley, 2005.|
|Prerequisites / Notice||Prerequisites:|
Control Systems or equivalent
Does not take place this semester.
|W||4 credits||2V + 1U||to be announced|
|Abstract||Probability. Stochastic processes. Stochastic differential equations. Ito. Kalman filters. St Stochastic optimal control. Applications in financial engineering.|
|Objective||Stochastic dynamic systems. Optimal control and filtering of stochastic systems. Examples in technology and finance.|
|Content||- Stochastic processes|
- Stochastic calculus (Ito)
- Stochastic differential equations
- Discrete time stochastic difference equations
- Stochastic processes AR, MA, ARMA, ARMAX, GARCH
- Kalman filter
- Stochastic optimal control
- Applications in finance and engineering
|Lecture notes||H. P. Geering et al., Stochastic Systems, Measurement and Control Laboratory, 2007 and handouts|
|227-0248-00L||Power Electronic Systems II||W||6 credits||4G||J. W. Kolar|
|Abstract||This course details structures, operating ranges, and control concepts of modern power electronic systems to provide a deeper understanding of power electronic circuits and power components. Most recent concepts of high switching frequency AC/DC converters and AC/AC matrix inverters are presented. Simulation exercises, implemented in GeckoCIRCUITS, are used to consolidate the concepts discussed.|
|Objective||The objective of this course is to convey knowledge of structures, operating ranges, and control concepts of modern power electronic systems. Further objectives are: to know most recent concepts and operation modes of high switching frequency AC/DC converters and AC/AC matrix inverters; to develop a deeper understanding of multi-pulse power converter circuits, transformers, and electromechanical energy converters; and to understand in-depth details of power electronic systems. Simulation exercises, implemented in the electric circuit simulator GeckoCIRCUITS, are used to consolidate the presented theoretical concepts.|
|Content||Converter dynamics and control: State Space Averaging, transfer functions, controller design, impact of the input filter on the converter transfer functions. |
Performance data of single-phase and three-phase systems: effect of different loss components on the efficiency characteristics, linear and non-linear single phase loads, power flow of general three-phase systems, space vector calculus.
Modeling and control of three-phase PWM rectifiers: system characterization using rotating coordinates, control structure, transfer functions, operation with symmetrical and unsymmetrical mains voltages.
Scaling laws of transformers and electromechanical actuators.
Drives with permanent magnet synchronous machines: basic function, modeling, field-oriented control.
Unidirectional AC/DC converters and AC/AC converters: voltage and current DC link converters, indirect and direct matrix converters.
|Lecture notes||Lecture notes and associated exercises including correct answers, simulation program for interactive self-learning including visualization/animation features.|
|Prerequisites / Notice||Prerequisites: Introductory course on power electronics.|
|227-0518-10L||Design and Control of Electric Machines||W||6 credits||4G||D. Bortis|
|Abstract||This course covers modeling and control concepts of modern drive systems and provides a deeper understanding of the dynamic operation of electric machines. Different aspects arising in the design of electric drive systems are investigated. The exercises are used to consolidate the concepts discussed.|
|Objective||The objective of this course is to convey knowledge on control strategies of different types of electric machines and on design principles of variable speed drive systems. A dynamic modeling of the electromechanical system is investigated, enabling the proper design of cascaded speed, torque/current controllers. Further objectives are the identification of machine parameters and a short insight into basic inverter circuits applied in advanced motor drive systems. Exercises are used to consolidate the presented theoretical concepts.|
|Content||1. Introduction to variable speed motor drive systems consisting of:|
- Electromechanical system
- Power electronic system
- Control system
- Measurement system
2. Control structures and strategies of DC Machine/Synchronous machine/Asynchronous machine/Brushless DC machine.
- Cascaded control
- U/f Control
- Slip Control
- Field-oriented control
3. Dynamic Operation of electric machines
- Dynamic modeling of electromechanical system
- Controller types and design
- Current/torque control
- Speed control (Voltage control / Flux weakening)
4. Power electronic inverter circuits in variable speed drive systems
- Voltage and current source inverter systems
- Basic operation and pulse width modulation
5. Identification of machine parameters
6. Design principles of variable speed motor drives systems
|Lecture notes||Lecture notes and associated exercises including correct answers|
|Prerequisites / Notice||Prerequisites: Fundamentals of Electric Machines|
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